基于改进SegFormer模型和多分辨率影像融合的耕地精细化提取方法OA

The fine extraction of cultivated land is of great significance for agricultural resource management and food security safeguarding.In order to improve the extraction accuracy and precision of cultivated land in the broken area of the plot,this study first constructed different datasets based on the high-resolution data of ZY-1 satellite and the medium-resolution data of Sentinel-2 satellite.Dataset 1 was com-posed of red band,green band and blue band data of ZY-1 satellite;Dataset 2 was composed of 12 band data upsam-pled from Sentinel-2 satellite image to YZ-1 satellite reso-lution;Dataset 3 was composed of 12 band data of Sentinel-2 after fusion with ZY-1 satellite image;Dataset 4 was com-posed of Dataset 1 and Dataset 2;Dataset 5 was composed of Dataset 1 and Dataset 3.Then,the original SegFormer model was optimized:the adaptive input layer design was adopted to make the encoder flexibly adapt to the number of spectral channels of multi-source remote sensing images,and the representation ability of the network in high-dimensional space was enhanced by increasing the depth of feature extraction.Instead of bilinear interpolation upsampling in the original decoder,the learnable transposed convolution was used to restore the spatial resolution while retaining more edge detail information,which could effectively prevent the blur,fracture or adhesion of the cultivated land boundary during the upsampling process.Finally,Gaoqing County of Zibo City of Shandong province was taken as the research area to clarify the influence of the original SegFormer model and the improved SegFormer model on the extraction accuracy of cultivated land under different datasets.The results showed that the use of fusion images could significantly improve the accuracy of cultivated land seg-mentation.Among them,using the fusion Dataset 5,the mean intersection over union(mIoU),average F1 score(mF1),and average pixel accuracy(mAcc)of the improved SegFormer model for cultivated land recognition accuracy were 3.32 percentage points,1.73 percentage points,and 1.77 percentage points higher than the Dataset 1,and were 3.50 percentage points,1.83 percentage points,and 1.86 percentage points higher than the Dataset 2,respectively.The fusion Dataset 5 showed the best effect.Using the Dataset 5,the improved SegFormer model's recognition accuracies of cultivated land mI-oU,mF1,and mAcc were 0.48 percentage points,0.25 percentage points,and 0.25 percentage points higher than the origi-nal model,respectively.In general,the data preprocessing scheme that first fused Sentinel image and then overlaid with ZY-1 image could greatly improve the cultivated land extraction accuracy of the improved SegFormer model.This study pro-vides a feasible technical method for high-precision farmland extraction and agricultural remote sensing monitoring.